Region of interest location and selective image compression

ABSTRACT

Systems and methods of operation for an image processor system to process images to locate two-dimensional regions which are likely to contain machine-readable symbol data or text. Such regions of interest (ROIs) may be preserved at full resolution, whilst the contents of non-ROIs are averaged into a single pixel value. Transition densities in an image may be converted into a numeric value. Such transition densities may be indicative of the presence of data of interest, such as textual data and/or machine-readable symbol data. The pixels values for the pixels in the ROIs may be sent to a decoder unchanged (i.e., full resolution), which absolves the decoder from having to perform any ROI location computations. Modified or altered images may be compressed to much smaller size files while maintaining lossless ROIs, which allows for transmission of such images to processor-based devices over a data communications channel in real time.

BACKGROUND

1. Technical Field

The present disclosure generally relates to image processing andcompression.

2. Description of the Related Art

Machine-readable symbols encode information in a form that can beoptically read via a machine-readable symbol reader or scanner.Machine-readable symbols take a variety of forms, the most commonlyrecognized form being the linear or one-dimensional machine-readablesymbol. Other forms include two-dimensional machine-readable symbolssuch as stacked code symbols, area or matrix code symbols, ormachine-readable symbols. These machine-readable symbols may be made ofpatterns of high and low reflectance areas. For instance, aone-dimensional barcode symbol may comprise a pattern of black bars on awhite background. Also for instance, a two-dimensional symbol maycomprise a pattern of black marks (e.g., bars, squares or hexagons) on awhite background. Machine-readable symbols are not limited to beingblack and white, but may comprise two other colors, and/or may includemore than two colors (e.g., more than black and white).

Machine-readable symbols are typically composed of elements (e.g.,symbol characters) which are selected from a particular machine-readablesymbology. Information is encoded in the particular sequence of shapes(e.g., bars) and spaces which may have varying dimensions. Themachine-readable symbology provides a mapping between machine-readablesymbols or symbol characters and human-readable symbols (e.g., alpha,numeric, punctuation, commands). A large number of symbologies have beendeveloped and are in use, for example Universal Product Code (UPC),International Article Number (EAN), Code 39, Code 128, Data Matrix,PDF417, etc.

Machine-readable symbols have widespread and varied applications. Forexample, machine-readable symbols can be used to identify a class ofobjects (e.g., merchandise) or unique objects (e.g., patents). As aresult, machine-readable symbols are found on a wide variety of objects,such as retail goods, company assets, and documents, and help trackproduction at manufacturing facilities and inventory at stores (e.g., byscanning objects as they arrive and as they are sold). In addition,machine-readable symbols may appear on a display of a portableelectronic device, such as a mobile telephone, personal digitalassistant, tablet computer, laptop computer, or other device having anelectronic display. For example, a customer, such as a shopper, airlinepassenger, or person attending a sporting event or theater event, maycause a machine-readable symbol to be displayed on their portableelectronic device so that an employee (e.g., merchant-employee) can readthe machine-readable symbol via a machine-readable symbol reader toallow the customer to redeem a coupon or to verify that the customer haspurchased a ticket for the event.

Machine-readable symbol readers or scanners are used to capture imagesor representations of machine-readable symbols appearing on varioussurfaces to read the information encoded in the machine-readable symbol.One commonly used machine-readable symbol reader is an imager- orimaging-based machine-readable symbol reader. Imaging-basedmachine-readable symbol readers typically employ flood illumination tosimultaneously illuminate the entire machine-readable symbol, eitherfrom dedicated light sources, or in some instances using ambient light.Such is in contrast to scanning or laser-based (i.e., flying spot) typemachine-readable symbol readers, which scan a relative narrow beam orspot of light sequentially across the machine-readable symbol.

Machine-readable symbol readers may be fixed, for example, readers maybe commonly found at supermarket checkout stands or other point of salelocations. Machine-readable symbol readers may also be handheld (e.g.,handheld readers or even smartphones), or mobile (e.g., mounted on avehicle such as a lift vehicle or a forklift).

Imaging-based machine-readable symbol readers typically includesolid-state image circuitry, such as charge-coupled devices (CCDs) orcomplementary metal-oxide semiconductor (CMOS) devices, and may beimplemented using a one-dimensional or two-dimensional imaging array ofphotosensors (or pixels) to capture an image of the machine-readablesymbol. One-dimensional CCD or CMOS readers capture a linearcross-section of the machine-readable symbol, producing an analogwaveform whose amplitude represents the relative darkness and lightnessof the machine-readable symbol. Two-dimensional CCD or CMOS readers maycapture an entire two-dimensional image. The image is then processed tofind and decode a machine-readable symbol. For example, virtual scanline techniques for digitally processing an image containing amachine-readable symbol sample across an image along a plurality oflines, typically spaced apart and at various angles, somewhat like ascan pattern of a laser beam in a scanning or laser-based scanner.

Images captured by imaging-based readers or cameras are often acquiredat a high resolution and are therefore relatively large. Thus, sendingimages from the camera to other processor-based devices in substantiallyreal-time over a data communications channel (e.g., LAN, WAN) can bedifficult or impossible. Various techniques have been used to addressthis problem. These techniques include utilizing one or more of imagedecimation (e.g., down sampling), image cropping and image compression(e.g., jpeg conversion) prior to transmission. However, using thesetechniques has disadvantages due to the loss of information in theimages. For instance, JPEG conversion, while providing a reasonablerepresentation of the original image, still destroys potentiallyimportant information in the image, such as binarized data encoded inthe pixel least significant bit (LSB), which may be used for opticalcharacter recognition (OCR), or embedded data typically placed in thescan line which provides information regarding camera operation relatingto the image being acquired. Further, image cropping is not alwaysreliable as part of the object or data of interest may be cropped.Cropping may also remove the aforementioned embedded data in images.

Additionally, known decoding software employs software modules whichfunction to locate regions of interest (ROIs) for machine-readablesymbol decoding. Such functions are computationally intensive, and suchfunctions do not assist in compression of the images.

BRIEF SUMMARY

A method of operation for an image processor system may be summarized asincluding receiving, by at least one processor, an original image filefrom at least one nontransitory processor-readable medium, the originalimage file comprising a two dimensional array of pixels, each of thepixels having an original pixel value; partitioning, by the at least oneprocessor, the original image file into a plurality of two dimensionalregions, each of the plurality of regions comprising a two dimensionalarray of the pixels; binarizing, by the at least one processor, theoriginal image file to generate a binarized image file comprising a twodimensional array of binarized pixels, each of the binarized pixelshaving a binarized pixel value; correlating, by the at least oneprocessor, each of the binarized pixels in the binarized image file by,for each binarized pixel, evaluating a binarization state of at leastone neighboring pixel; identifying, by the at least one processor, whichof the binarized pixels in the binarized image have a correlation valueabove a correlation threshold; generating, by the at least oneprocessor, a region correlation array comprising a plurality of regioncorrelation values, each region correlation value corresponding to oneof the plurality of regions and indicative of the number of binarizedpixels in the corresponding region identified as having a correlationvalue above the correlation threshold; for each region, comparing, bythe at least one processor, the region correlation value with a regionof interest threshold; and identifying, by the at least one processor,the region as a region of interest if the region correlation value isabove the region of interest threshold. Binarizing the original imagefile may include implementing an edge detection algorithm to generate abinarized image file including a two dimensional array of binarizedpixels, each of the binarized pixels having a binarized pixel valueindicative of whether the binarized pixel has been classified as an edgeby the edge detection algorithm.

The method may further include blurring, by the at least one processor,the region correlation array using a convolution operation to generate ablurred region correlation array comprising a plurality of blurredregion correlation values; wherein, for each region, comparing theregion correlation value with a region of interest threshold comprisescomparing the blurred region correlation value with a region of interestthreshold; and identifying the region as a region of interest if theregion correlation value is above the region of interest thresholdcomprises identifying the region as a region of interest if the blurredregion correlation value is above the region of interest threshold.

The method may further include responsive to identifying the region as aregion of interest, identifying, by the at least one processor, eachregion adjacent the region as a region of interest.

The method may further include computing, by the at least one processor,a region average pixel value for each of a plurality of non-regions ofinterest, each non-region of interest being one of the regions of theoriginal image file not identified as a region of interest; for eachpixel in a non-region of interest, setting, by the at least oneprocessor, the pixel value to the computed region average pixel valuefor the region of which the pixel is a part to generate a modified imagefile; and storing, by the at least one processor, the modified imagefile in the at least one nontransitory processor-readable medium.

The method may further include sending, by the at least one processor,the modified image file to a decoder for decoding of the modified imagefile.

The method may further include decoding, by the at least one processor,the modified image file.

The method may further include compressing, by the at least oneprocessor, the modified image file to generate a compressed modifiedimage file.

The method may further include sending, by the at least one processor,the compressed modified image file to a processor-based device over atleast one data communication channel.

Compressing the modified image file may include, for each non-region ofinterest of the original image file, storing, by the at least oneprocessor, a single pixel value for all of the pixels in the non-regionof interest.

An image processor system may be summarized as including at least oneprocessor; at least one nontransitory processor-readable storage mediumoperatively coupled to the at least one processor which stores anoriginal image file comprising a two dimensional array of pixels, eachof the pixels having an original pixel value, the at least onenontransitory processor-readable medium further storing at least one ofdata or instructions which, when executed by the at least one processor,cause the at least one processor to: partition the original image fileinto a plurality of two dimensional regions, each of the plurality ofregions comprising a two dimensional array of the pixels; identify oneor more regions of interest in the original image file, the one or moreregions of interest comprising one or more regions wherein data ofinterest are likely to be found; compute a region average pixel valuefor each of a plurality of non-regions of interest, each non-region ofinterest being one of the regions of the original image file notidentified as a region of interest; for each pixel in a non-region ofinterest, set the pixel value to the computed region average pixel valuefor the region of which the pixel is a part to generate a modified imagefile; and store the modified image file in the at least onenontransitory processor-readable medium.

The at least one processor may binarize the original image file togenerate a binarized image file comprising a two dimensional array ofbinarized pixels, each of the binarized pixels having a binarized pixelvalue; correlate each of the binarized pixels; identify which of thebinarized pixels have a correlation value above a correlation threshold;and generate a region correlation array comprising a plurality of regioncorrelation values, each region correlation value corresponding to oneof the plurality of regions and indicative of the number of binarizedpixels in the corresponding region identified as having a correlationvalue above the correlation threshold. The at least one processor may,for each of the binarized pixels, evaluate a binarization state of atleast one neighboring pixel to correlate the binarized pixel. The atleast one processor may blur the region correlation array using aconvolution operation to generate a blurred region correlation array ofblurred region correlation values. The at least one processor may, foreach region, compare the region correlation value with a region ofinterest threshold; and identify the region as a region of interest ifthe region correlation value is above the region of interest threshold.The at least one processor may, for each region in which the regioncorrelation value is above the region of interest threshold, identifyeach region adjacent the region as a region of interest. The data ofinterest may include at least one of text or a machine-readable symbol.The at least one processor may send the modified image file to a decoderfor decoding of the data of interest present in the modified image file.The at least one processor may decode the data of interest present inthe modified image file. The at least one processor may compress themodified image file to generate a compressed modified image file. The atleast one processor may send the compressed modified image file to aprocessor-based device over at least one data communication channel. Theat least one processor may, for each non-region of interest of theoriginal image file, store a single pixel value for all of the pixels inthe non-region of interest. The at least one processor may send themodified image file to a processor-based device via at least one datacommunication channel.

A method of operation for an image processor system may be summarized asincluding receiving, by at least one processor, an original image filefrom at least one nontransitory processor-readable medium, the originalimage file comprising a two dimensional array of pixels, each of thepixels having an original pixel value; partitioning, by the at least oneprocessor, the original image file into a plurality of two dimensionalregions, each of the plurality of regions comprising a two dimensionalarray of the pixels; identifying, by the at least one processor, one ormore regions of interest in the original image file, the one or moreregions of interest comprising one or more regions wherein data ofinterest are likely to be found; computing, by the at least oneprocessor, a region average pixel value for each of a plurality ofnon-regions of interest, each non-region of interest being one of theregions of the original image file not identified as a region ofinterest; for each pixel in a non-region of interest, setting, by the atleast one processor, the pixel value to the computed region averagepixel value for the region of which the pixel is a part to generate amodified image file; and storing, by the at least one processor, themodified image file in the at least one nontransitory processor-readablemedium.

Identifying one or more regions of interest in the original image filemay include binarizing, by the at least one processor, the originalimage file to generate a binarized image file comprising a twodimensional array of binarized pixels, each of the binarized pixelshaving a binarized pixel value; correlating, by the at least oneprocessor, each of the binarized pixels; identifying, by the at leastone processor, which of the binarized pixels have a correlation valueabove a correlation threshold; and generating, by the at least oneprocessor, a region correlation array comprising a plurality of regioncorrelation values, each region correlation value corresponding to oneof the plurality of regions and indicative of the number of binarizedpixels in the corresponding region identified as having a correlationvalue above the correlation threshold. Correlating each of the binarizedpixels may include, for each of the binarized pixels, evaluating, by theat least one processor, a binarization state of at least one neighboringpixel. Identifying one or more regions of interest in the original imagefile may include blurring, by the at least one processor, the regioncorrelation array using a convolution operation to generate a blurredregion correlation array of blurred region correlation values.Identifying one or more regions of interest in the original image filemay include, for each region, comparing, by the at least one processor,the region correlation value with a region of interest threshold; andidentifying, by the at least one processor, the region as a region ofinterest if the region correlation value is above the region of interestthreshold. Identifying one or more regions of interest in the originalimage file may include, for each region in which the region correlationvalue is above the region of interest threshold, identifying, by the atleast one processor, each region adjacent the region as a region ofinterest. Identifying one or more regions of interest in the originalimage file may include identifying, by the at least one processor, oneor more regions of interest comprising one or more regions wherein dataof interest are likely to be found, and the data of interest comprisesat least one of text or a machine-readable symbol.

The method may further include sending, by the at least one processor,the modified image file to a decoder for decoding of the data ofinterest present in the modified image file.

The method may further include decoding, by the at least one processor,the data of interest present in the modified image file.

The method may further include compressing, by the at least oneprocessor, the modified image file to generate a compressed modifiedimage file.

The method may further include sending, by the at least one processor,the compressed modified image file to a processor-based device over atleast one data communication channel. Compressing the modified imagefile may include, for each non-region of interest of the original imagefile, storing, by the at least one processor, a single pixel value forall of the pixels in the non-region of interest.

The method may further include sending, by the at least one processor,the modified image file to a processor-based device via at least onedata communication channel.

BRIEF DESCRIPTION OF THE SEVERAL VIEWS OF THE DRAWINGS

In the drawings, identical reference numbers identify similar elementsor acts. The sizes and relative positions of elements in the drawingsare not necessarily drawn to scale. For example, the shapes of variouselements and angles are not necessarily drawn to scale, and some ofthese elements may be arbitrarily enlarged and positioned to improvedrawing legibility. Further, the particular shapes of the elements asdrawn, are not necessarily intended to convey any information regardingthe actual shape of the particular elements, and may have been solelyselected for ease of recognition in the drawings.

FIG. 1 is a perspective view of a machine-readable symbol reader andtarget object, according to one illustrated implementation.

FIG. 2 is a functional block diagram of a machine-readable symbolreader, according to one illustrated implementation.

FIG. 3 is a flow diagram of a high-level method of operation for animage processor system, according to one illustrated implementation.

FIG. 4 is a flow diagram of a low-level method of operation for an imageprocessor system, according to one illustrated implementation.

FIG. 5 is a diagram depicting a target pixel of an image and a pluralityof neighboring pixels surrounding the target pixel, according to oneillustrated implementation.

FIG. 6 is a diagram depicting a plurality of regions of pixels of animage, according to one illustrated implementation.

FIG. 7 is a flow diagram of a method of operation for an image processorsystem to compress an image, according to one illustratedimplementation.

FIG. 8 is a flow diagram of a method of operation for an image processorsystem to compress an image, according to one illustratedimplementation.

FIG. 9 is a diagram depicting an example data structure generated usingthe method FIG. 8, according to one illustrated implementation.

FIG. 10 is a diagram depicting directional values for a 3×3 region ofpixels, according to one illustrated implementation.

FIG. 11 is a diagram depicting pattern values for a 3×3 region ofpixels, according to one illustrated implementation.

FIG. 12 is a diagram depicting a look up table calibration pattern,according to one illustrated implementation.

FIG. 13 is a diagram depicting a bitmap which implements a look uptable, according to one illustrated implementation.

FIG. 14 is a diagram of a calibration target response graph, accordingto one illustrated implementation.

FIG. 15 is a diagram depicting an angle calculation histogram for aplurality of regions of an image, according to one illustratedimplementation.

FIG. 16 is a flow diagram of a method of operation for an imageprocessor system to detect text, a one-dimensional machine-readablesymbol and a two dimensional machine-readable symbol in an image,according to one illustrated implementation.

DETAILED DESCRIPTION

In the following description, certain specific details are set forth inorder to provide a thorough understanding of various disclosedimplementations. However, one skilled in the relevant art will recognizethat implementations may be practiced without one or more of thesespecific details, or with other methods, components, materials, etc. Inother instances, well-known structures associated with computer systems,server computers, and/or communications networks have not been shown ordescribed in detail to avoid unnecessarily obscuring descriptions of theimplementations.

Unless the context requires otherwise, throughout the specification andclaims that follow, the word “comprising” is synonymous with“including,” and is inclusive or open-ended (i.e., does not excludeadditional, unrecited elements or method acts).

Reference throughout this specification to “one implementation” or “animplementation” means that a particular feature, structure orcharacteristic described in connection with the implementation isincluded in at least one implementation. Thus, the appearances of thephrases “in one implementation” or “in an implementation” in variousplaces throughout this specification are not necessarily all referringto the same implementation. Furthermore, the particular features,structures, or characteristics may be combined in any suitable manner inone or more implementations.

As used in this specification and the appended claims, the singularforms “a,” “an,” and “the” include plural referents unless the contentclearly dictates otherwise. It should also be noted that the term “or”is generally employed in its sense including “and/or” unless the contentclearly dictates otherwise.

The headings and Abstract of the Disclosure provided herein are forconvenience only and do not interpret the scope or meaning of theimplementations.

Implementations of the present disclosure are directed to systems andmethods for processing video or image data to locate and preserve twodimensional regions of an image which are likely to containmachine-readable symbol data or text, referred to herein as regions ofinterest (ROIs), whilst averaging the contents of those regions whichare not likely to contain machine-readable symbol data or text, referredto herein as non-ROIs, into a single pixel value. For example, in someimplementations, transition densities in an image are converted into anumeric value. Such transition densities may be indicative of thepresence of data of interest, such as textual data and/ormachine-readable symbol data. The pixel values for the pixels in theROIs may be sent to a decoder unchanged (i.e., at their fullresolution). This feature absolves the decoder from having to performany ROI computations.

In some implementations, modified or altered images may be compressedusing a standard compression technique, such as JPEG conversion. In someimplementations, an altered image may be selectively compressed becauseeach of the non-ROIs may be defined by a single value (e.g., one byte).In instances where regions are 16×16 pixels, for example, each non-ROIallows for a 99.6% compression ratio since the pixel values for all 256pixels in the non-ROI may be represented by the single value. In theseimplementations, the pixel values of the pixels in the ROIs arepreserved, along with any embedded data, without loss. As discussedfurther below with reference to FIG. 8, such compression may be achievedby creating a bit-wise map of the image indicating which regions areROIs and which regions are non-ROIs. Using this scheme, images may becompressed while maintaining lossless ROIs. It should be appreciatedthat this second compression implementation may require customdecompression software on processor-based devices which need todecompress such images.

Using the systems and methods disclosed herein, images may be compressedto a much smaller size, which allows for transmission of such images tousers over a data communications channel with the ROIs preserved attheir full resolution, while not consuming a large amount of bandwidthtransmitting non-ROI portions at their full resolution.

FIG. 1 shows a machine-readable symbol reader 100 acting upon a targetobject 102 labeled with a machine-readable symbol 104. The reader orscanner 100 may be an imaging based machine-readable symbol reader(e.g., camera). In use, the target object 102 having themachine-readable symbol 104 may be brought within a field of view (FV)of the machine-readable symbol reader via a conveyor 106 to have thereader 100 read or detect the machine-readable symbol 104.

In some implementations, the machine-readable symbol reader 100 mayinclude a flood illumination system 108 to illuminate the targetmachine-readable symbol 104. The flood illumination system 108 maydirect a flood illumination pattern towards the target machine-readablesymbol 104. The illumination from the flood illumination system 108 maybe reflected by the target machine-readable symbol 104. The reflectedlight then passes through an imaging system 110 of the reader 100 (seeFIG. 2).

The machine-readable symbol reader 100 may be communicatively coupled toone or more externals systems, such as a host system 112, via one ormore wired and/or wireless communications networks 114 (e.g., intranet,Internet). The machine-readable symbol reader 100 may send images and/ordata (e.g., decoded machine-readable symbol data) to the one or moreexternal systems 112 via the one or more networks 114. Themachine-readable symbol reader 100 may also receive at least one ofinstructions or data from the one or more external systems 112, such asconfiguration instructions or data, via the one or more networks 114.

FIG. 2 is a block diagram of an imaging based machine-readable symbolreader 200 in accordance with some implementations of the presentdisclosure. The machine-readable symbol reader 200 may be similar oridentical to the machine-readable symbol reader 100 of FIG. 1. Themachine-readable symbol reader 200 includes an imaging system 202 whichcaptures image frames of graphical indicia such as the machine-readablesymbol 104 of FIG. 1 present in the field of view (FV) of the imagingsystem 202. The reader 200 also includes one or more processors 204operatively coupled to a nontransitory processor-readable medium 206which stores ROI location logic 208, compression logic 210 and decoderlogic 211. The decoder logic 211 decodes encoded indicia within acaptured image frame. For example, the decoder logic 211 may beoperative to decode various symbols and/or text, such as, Aztec,Codabar, Code 11, Code 128, Code 39, Code 93, Data Matrix, Postal, UPC,etc. The ROI location logic 208, compression logic 210 and decoder logic211 may be executed by the processor 204, for example. In someimplementations, one or more of the ROI location logic 208, compressionlogic 210 and decoder logic 211 are implemented by multiple processors,by hardware, or by any combination thereof.

The imaging system 202 includes imaging circuitry 212, focusing opticsincluding one or more imaging or focusing lens 214, and a photo sensoror pixel array 216. The focusing lens 214 focuses light reflected andscattered from the target machine-readable symbol 104 through anaperture onto the pixel/photo sensor array 216. Thus, the lens 214focuses an image of the target machine-readable symbol 104 (assuming thewatermark is within the FV) onto the array of pixels comprising thepixel array 216, thereby enabling the pixel array to capture an image ofa target object 102 within a FV of the imaging system during an exposureperiod. The FV of the imaging system 202 includes both a horizontal anda vertical field of view. The FV of the imaging system 202 may be afunction of both the configuration of the sensor array 216 and theoptical characteristics of the imaging lens 214 and the distance andorientation between the array 216 and the imaging lens.

The sensor array 216 may include a charged coupled device (CCD), acomplementary metal oxide semiconductor (CMOS), or other imaging pixelarray, operating under the control of the imaging circuitry 212. Thesensor array 216 may have various width and height dimensions in pixels,such as 640×480 pixels, 752×480 pixels, 1280×1024 pixels, 1600×1200,8192×1 (e.g., high speed linear array), other combinations of width andheight dimensions, or any sub-windows thereof.

In some implementations, the machine-readable symbol reader 200 mayinclude a flood illumination system 230 to illuminate the targetmachine-readable symbol 104. The flood illumination system 230 maydirect a flood illumination pattern towards the target machine-readablesymbol 104. The illumination from the flood illumination system 230 maybe reflected by the target machine-readable symbol 104. The reflectedlight then passes through the imaging lens 214 and is focused onto thesensor array 216 of the imaging system 202.

As discussed further below, the decoder logic 210 may decode anydecodable image within one or more images captured by the imaging system202. If the decoding is successful, decoded data, representative of thedata/information coded in the machine-readable symbol 104, may thenoutput via a data input/output system 232, which may include one or moreof a display, LEDs, an audio output, touchscreen, keys, buttons, etc.Upon a successful imaging and decoding of the machine-readable symbol104, the input/output system 232 may provide feedback to the operator inthe form of a visual indicator and/or an audible indicator.

The reader 200 may also include one or more wired and/or wirelesscommunications ports 234 which communicatively couple the reader to oneor more external systems 236, such as the host system 112 shown in FIG.1, via one or more suitable wired/wireless communications networks orchannels. The reader 200 may send images and or data to the externalsystems 236, as discussed further below.

The ROI location logic 208, compression logic 210 and decoder logic 211may be implemented in any suitable manner, including hardware, software,electrical circuitry, firmware, on an application specific integratedcircuit (ASIC), on a programmable gate array (PGA), or any combinationthereof. Particular algorithms for implementing the ROI location logic208, compression logic 210 and decoder logic 211 are discussed belowwith reference to FIGS. 3-16.

FIG. 3 is a flow diagram of a high-level method 300 for an imageprocessor system which facilitates both improved decoding times andimproved levels of compression. The method 300 may be executed by amachine-readable symbol reader, such as the machine-readable symbolreaders 100 and 200 of FIGS. 1 and 2, respectively.

At 302, at least one processor of the reader receives an original imagewhich captures at least one machine-readable symbol. For example, the atleast one processor of the reader may receive the original image from animaging subsystem of the reader. The original image may include atwo-dimensional array of pixels, such as an array having a width of 8192pixels and a height of 8192 pixels, which provides a total of 67,108,864pixels. Such an image may be obtained by accumulating line by line theoutput of a 8192×1 linear array, acquired in a temporal sequence whilean object is moving in the field of view of the imaging system. Each ofthe pixels in the image may have a grayscale value associated therewith,which grayscale value may have any suitable bit depth (e.g., 8 bits, 10bits).

At 304, the at least one processor locates regions likely to includemachine-readable symbol data, referred to as regions of interest (ROIs),in the original image. For example, the at least one processor,executing the ROI location logic may partition the original image into aplurality of two-dimensional regions each having a pixel width andheight (e.g., 16×16 pixels, 20×12 pixels). The at least one processormay determine which of those regions are ROIs and which of those regionsare non-ROIs. An exemplary method for locating ROIs in the originalimage is shown in FIG. 4 and discussed below.

At 306, the at least one processor computes an average pixel value forthe pixels in each of the non-ROIs. For example, such may be achievedby, for each non-ROI, summing the grayscale values of each of the pixelsin the non-ROI, and dividing the sum by the number of pixels in thenon-ROI. Thus, all of the pixels in a particular non-ROI are representedby the same grayscale value.

At 308, the at least one processor generates a modified image whichincludes the original pixel values for each of the pixels located in anROI, and the computed average pixel values for each of the pixelslocated in a non-ROI. Thus, the modified image retains the relativelyhigh resolution of the original image for the ROIs and provides arelatively low resolution for the non-ROIs, which provides significantstorage requirement savings for the modified image.

At 310, the at least one processor may send the modified image to adecoder which decodes the machine-readable symbol data present in themodified image.

At 312, the at least one processor may compress the modified image usingany number of compression algorithms. Since the non-ROIs are representedat a low resolution, compression algorithms may be able to provide muchgreater compression for the modified image compared to performing thesame compression algorithm on the original image. Two compressionalgorithms are shown in FIGS. 7 and 8 and are discussed below.

At 314, the at least one processor may transmit the compressed modifiedimage to an external processor-based device over a data communicationschannel. For example, the at least one processor may transmit thecompressed modified image file to one or more processor-based devicesover a wired and/or wireless LAN or WAN. Since the compressed modifiedimage is much smaller in size than the original image, the reader mayoffload the modified image in substantially real time to one or moreexternal devices for viewing, further processing, storage, etc. It isnoted that in some implementations decoding may be performed internally,before compression, or externally, on the compressed image.

FIG. 4 is a flow diagram of a low-level method 400 of operation for animage processor system which facilitates improved decoding times andimproved levels of compression. The method 400 may be executed by one ormore processors of a machine-readable symbol reader, for example, suchas the machine-readable symbol readers 100 and 200 of FIGS. 1 and 2,respectively, executing ROI location logic.

At 402, at least one processor receives an original image which capturesat least one machine-readable symbol. For example, the at least oneprocessor of the reader may receive the original image from an imagingsubsystem of the reader, from a nontransitory processor-readable mediumof the reader or from another processor-based device communicablycoupled to the reader. The original image may include a two dimensionalarray of pixels, such as an array having a width of 8192 pixels and aheight of 8192 pixels, which provides a total of 67,108,864 pixels. Eachof the pixels in the image may have a grayscale value associatedtherewith, which grayscale value may have any suitable bit depth (e.g.,8 bits, 10 bits).

At 404, the at least one processor binarizes the original image tocreate a binarized image. In some implementations, the at least oneprocessor may utilize a binarization algorithm which is a hardwareimplemented edge detector that accurately detects edges within theoriginal image over a wide range of image quality. In someimplementations, pixels which are classified as an edge have the LSB oftheir grayscale value set to 0, while any pixels not classified as anedge have the LSB of their grayscale value set to 1.

In some implementations, the binarization algorithm utilizes a slidingwindow (e.g., an 8×8 pixel window) which passes over every pixel in theimage. For each pixel under evaluation, all of the pixels in the windoware summed, and the darkest and brightest pixel values are stored. Thistotal summation is then recomputed as:

total=(total−(total/16.0)/64.0)

If the value of the pixel being evaluated is greater than “total,” or ifthe difference between the brightest pixel and the darkest pixel in thewindow is less than a value (e.g., 32), then the LSB of the grayscalevalue of the pixel being evaluated is set to 0, indicating the pixelunder evaluation is an edge. Otherwise, the LSB of the grayscale valueof the pixel being evaluated is set to 1, indicating the pixel underevaluation is not an edge. This LSB may be referred to as the pixel's“binarized state” or “binarization state,” which signifies whether thepixel is classified as an “edge” or a “non-edge.” It should beappreciated that other binarization algorithms may be utilized.Generally, the inventor of the present disclosure has found that thenumber of pixels classified as edges in a given region is higher whenmachine-readable symbols or textual data are present.

At 406, the at least one processor partitions the binarized image into aplurality of regions. For example, the at least one processor maypartition the original image into a plurality of two-dimensional regionseach having a pixel width of 16 pixels and a pixel height of 16 pixels.Other dimensions for the regions may also be utilized.

At 408, the at least one processor determines a correlation for each ofthe pixels in the binarized image. In some implementations, each pixelis correlated by counting the binarization of the immediate eightneighbors of the pixel under consideration (“target pixel”). Forexample, FIG. 5 shows an example 3×3 pixel region 500 of nine pixels,namely, pixels P₁₋₈ and P_(T). For a target pixel P_(T) underconsideration, the correlation algorithm may consider the binarizationof the eight immediate neighboring pixels P₁₋₈ of the target pixelP_(T).

In some implementations, the counting process also considers pixels insurrounding regions to accommodate target pixels located on regionboundaries. For example, if the target pixel P_(T) of FIG. 5 wereadjacent a region boundary (e.g., a lower boundary), the countingprocess may still consider pixels P₁₋₈ although some (e.g., pixels P₆₋₈)of the pixels P₁₋₈ may be outside the region in which the target pixelP_(T) is located. In some implementations, the counting processconsiders pixels in surrounding regions in all cases except when thetarget pixel is at the actual image boundary.

At 410, binarized pixels which have at least a correlation threshold(‘x’) of binarized neighbors get counted or retained as “correlatedpixels.” For example, if x=0, then all binarized pixels would be countedor retained as correlated pixels. If x>8, then no binarized pixels wouldbe counted as correlated pixels. Setting the correlation threshold x=2ensures that marginally binarized symbols (e.g., width of one pixel)would be counted. In such instances, only single binarized pixels orpairs of binarized pixels would be suppressed. However, such a lowcorrelation threshold is not often needed.

For example, even at 1.4 pixels per module (PPM), a single module (e.g.,a bar of a barcode) is at least two binarized pixels wide. Thus, mostpixels on narrow modules will have 5 binarized pixel neighbors. Forexample, the target pixel P_(T) may have binarized neighbors P₁, P₂, P₄,P₆ and P₇, for a vertically oriented bar module or may have binarizedneighbors P₁, P₂, P₃, P₄, and P₅ for a horizontally oriented bar module.

In some implementations, the correlation threshold may be set to 5,which has been found to be effective at suppressing non-machine-readablesymbol artifacts, such as wood grain found on some tilt-tray systems.

Other methods may be used to test for correlation. For example, modulesin a given region may produce very bright bin values when a HoughTransform is calculated. Similarly, neighboring regions may have theirrespective Hough Transform compared as well. Other correlations may alsobe used.

At 412, the at least one processor computes or counts the number ofcorrelated binarized pixels in each region of the image. For example,FIG. 6 shows a group 600 of regions R₁₋₁₆ for an example image. In thecase where each of the regions R₁₋₁₆ has a dimension of 16×16 pixels,each of the regions comprises 256 pixels of the original image. The atleast one processor may count the number (between 0 and 256) ofcorrelated binarized pixels in each of the regions R₁₋₁₆. Thus, eachregion may be represented by a number, referred to herein as a regioncorrelation value, which creates a two dimensional array of regioncorrelation values, referred to herein as a region correlation array.Each of the region correlation values may be indicative of the number ofcorrelated pixels in each corresponding region.

Optionally, at 414 the calculated regions are convolved (blurred) using3×3 averaging. In some implementations wherein the regions arecalculated row by row, after each three rows of the regions have beencalculated, the rows of regions are convolved “on the fly” using 3×3averaging in substantially real time. This blurring operation may makemasses of regions which form machine-readable symbols appear moreuniform while at the same time lowering the value of small groups ofROIs which may make up object edges, scuff marks, etc. As indicatedabove, in some implementations the blurring or smoothing act 414 may beomitted.

At 416, after correlation and optional blurring, the at least oneprocessor determines, for each region, whether the number of retainedbinarized pixels in each region is greater than or equal to a region ofinterest threshold (“ROI threshold”). At 418, if the number of retainedbinarized pixels in a region is greater than or equal to the ROIthreshold (i.e., 416=‘yes’), the at least one processor sets oridentifies the region as a ROI.

At 420, if the number of retained binarized pixels in a region is lessthan the ROI threshold (i.e., 416=‘no’), the at least one processor maydetermine, for each such region, whether any of the regions immediatelyadjacent the region meet the ROI threshold. For regions which haveimmediately adjacent regions which meet the ROI threshold (i.e.,420=‘yes’), the at least one processor sets or identifies such regionsas ROIs. For example, if the region R₆ of FIG. 6 is determined to notmeet the ROI threshold, the region R₆ may still be identified as an ROIif any of the adjacent eight regions R₁, R₂, R₃, R₅, R₇, R₉, R₁₀, andR₁₁ meet the ROI threshold. This feature may be used to handle boundaryconditions where a particular region at a boundary of a machine-readablesymbol may include only a single bar (e.g., the first bar) of themachine-readable symbol and thus may fall below the ROI threshold. Inthis example, the criteria for making a region an ROI if the region doesnot pass the ROI threshold is to determine if any of the region's eightimmediate neighbors exceeds the ROI threshold (not just marked asvalid). If such is true, then the region in question is also consideredan ROI. It is noted that the act 420 is not a mandatory. For example, insome implementations, such act may be implemented by an external decoderafter the determined ROIs are transmitted to another component orsystem.

At 422, for each of the regions not determined to be an ROI (“non-ROIregions”), the at least one processor may compute an average pixel valuefor the all of the pixels in the non-ROI. For example, such may beachieved by, for each non-ROI, summing the grayscale values of each ofthe 256 pixels in the non-ROI, and dividing the sum by 256 (i.e., thenumber of pixels in the non-ROI). Thus, all of the pixels in aparticular non-ROI are represented by a single grayscale value.

At 424, the at least one processor may generate a modified image whichincludes original pixel data from the original image for the pixels inthe ROIs and includes the averaged pixel values for pixels in thenon-ROIs.

In some implementations, when the correlation threshold is set to 5, theROI threshold may be set to a value that is at least 30 to suppress themajority of unwanted artifacts. In some implementations, the ROIthreshold may be set to 40, 60 or higher.

FIG. 7 is a flow diagram of a method 700 of operation for an imageprocessor system to compress an image. At 702, at least one processor ofa processor-based device, such as the machine-readable symbol readers100 and 200 of FIGS. 1 and 2, respectively, receives a modified image.The modified image may be modified utilizing the region of interestlocation methods 300 or 400 of FIGS. 3 and 4, respectively.

At 704, the at least one processor may compress the modified image usinga standard image compression technology. For example, the at least oneprocessor may compress the modified image using JPEG compression. Sincethe modified image includes numerous non-ROIs which each have a number(e.g., 256) of pixels set to the same value, the standard imagecompression algorithm is able to generate a highly compressed imagecompared to compressing the original image which has not been modifiedusing the methods described above.

FIG. 8 is a flow diagram of a method 800 of operation for an imageprocessor system to compress an image. FIG. 9 provides an illustrativeexample of a data structure 900 generated using the compression method800 of FIG. 8. At 802, at least one processor of a processor-baseddevice, such as the machine-readable symbol reader 100 and 200 of FIGS.1 and 2, respectively, receives a modified image. The modified image maybe modified utilizing the region of interest location methods 300 or 400of FIGS. 3 and 4, respectively. The modified image includes a pluralityof ROIs and a plurality of non-ROIs. As discussed above, each of thepixels in the ROIs retain their original grayscale values, while each ofthe pixels in a non-ROI have a value which is the average pixel valuefor all the pixel values of the particular non-ROI.

At 804, the at least one processor stores whether each region of animage is an ROI or a non-ROI as a bit value in a nontransitoryprocessor-readable medium. For example, an 8192 pixels wide imagecontains a plurality of rows, each row comprising 512 16×16 pixelregions. Thus, a series of 512 bits (i.e., 64 bytes) may be used toindicate which regions in a row of regions are ROIs and which regionsare non-ROIs. As shown in FIG. 9, for each of X scan groups 902, bytes 0to 63 are used as a preamble 904 to indicate whether each of the regions0 to 511 are ROIs or non-ROIs.

Following the preamble 904 is the grayscale data 906 associated witheach of the regions 0 to 511. At 806, if the bit in the 64-byte field is1, then the corresponding region is an ROI, and all of the 256 grayscalevalues for the 256 pixels in the region are used to define the pixels inthe region. At 808, if the bit in the 64-byte field is 0, then thecorresponding region is a non-ROI and a single grayscale value is usedto define all of the pixels in the region. In some implementations, thefirst N (e.g., 3) bits of byte 0 may be set to 1 so embedded data 908 ispreserved.

For regions having a height of 16 pixels, this process may continue forevery 16 scan lines acquired by the machine-readable symbol reader. Insome implementations, the generated stream of data may be sent to thedecoder as is and transmitted out of the reader to an externalprocessor-based device. A receiving processor-based device may utilizean appropriately configured decompression algorithm to form the modifiedimage. This method provides excellent compression with losslesspreservation of the pixel data in the ROIs.

FIGS. 10-17 show various illustrations for a method of detecting textand/or machine-readable symbols in images. The method may be implementedin a machine-readable symbol reader or another suitable processor-baseddevice. The method discriminates between one-dimensionalmachine-readable symbols, two-dimensional machine-readable symbols, andtext. The method also provides an orientation angle for one-dimensionalmachine-readable symbols (e.g., barcodes). Generally, the method makesdecisions based solely on statistical distributions of binarized pixels.The method does not analyze or utilize grayscale values, yet the methodstill provides excellent discriminatory ability.

Prior to referencing FIGS. 10-17, a general description of the method isprovided. At least one processor analyzes 3×3 pixel regions across anentire image to determine the exact pattern of binarized pixels in theimage. Each pattern may be assigned a value (e.g., 0-511) depending onwhich pixels in the 3×3 region are binarized.

A well-designed look-up table with 512 entries utilizes the patternvalue of the region as an index into the look-up table. The output ofthe look-up table may be a directional value between −1 and 8, withvalues of 0 to 7 indicating directions, a value of −1 indicating that nodiscernable direction exists, and a value of 8 indicating multipledirections exist.

The at least one processor may create a vector image or vector map usingthe outputs from the look-up table. Such image may be divided intoblocks (e.g., 16×16 blocks). Within each block, the at least oneprocessor may build a histogram of directions (i.e., the look-up tableoutputs). This histogram may be analyzed for the mode of the histogram.

The method discussed below may be implemented as a relativelysophisticated correlation scheme. Further, careful design of the look-uptable allows this method to be used as a powerful featureidentification/extraction tool.

Referring now to FIG. 10, a diagram depicting directional values for a3×3 region 1000 of pixels 1002 is shown. As shown, there are 8 possibledirections (0-7) which can be assigned based upon pixel configuration. Avalue of −1 indicates no direction, and a value of 8 indicates multipledirections. The angular distance between each of the directions is 22.5degrees.

FIG. 11 is a diagram 1100 depicting pattern values 1102 for a 3×3 region1104 of pixels centered on a target pixel 1106. As shown, each 16×16pixel region will have 256 pattern values. In this example, the pixelsin the top row of the 3×3 region 1104 are assigned values of 1, 2 and 4from left to right. The pixels in the middle row of the 3×3 region areassigned values 8, 16 and 32 from left to right. The pixels in thebottom row are assigned values of 64, 128 and 256 from left to right. Inthis example, the 3×3 region 1104 surrounding the target pixel 1106 isassigned a value of 201 based on which of the pixels surrounding thetarget pixel are high (e.g., 1). As discussed further below, the value201 may be mapped via a look-up table to the directional value of 7, asindicated by an arrow 1108. It is noted that parallel lines may onlycontain a few possible shapes.

The result of calculating the pattern values is a vector map or vectorimage. That is, the pixel values for the image are a function of therespective vector which the pixels represent.

FIG. 12 is a diagram depicting a star-shaped look up table calibrationpattern 1200. In this example, the minimum angle of resolution is22.5*(1/16)=1.4 degrees. Thus, the pattern 1200 comprises a star patternwith spokes 1202 which are 1.4 degrees wide and which are spaced apartfrom each other by 1.4 degree gaps 1204. Generally, a histogram of thecalibration pattern 1200 should be as flat as possible across directions0 to 7.

FIG. 13 is a diagram depicting a bitmap 1300 which implements a look uptable. The bitmap 1300 contains rows of 3×3 regions of pixelconfigurations for all possible directions 0 to 7. The layout of thesepixel configurations may be modified to achieve the flattest directionresponse possible when applied to the “star” calibration pattern 1200FIG. 12.

FIG. 14 is a diagram of a calibration target response graph 1400, whichshows the response of the look-up table 1300 of FIG. 13 to the starcalibration pattern 1200 of FIG. 12 for directions 0-7. In someimplementations, vector amplitude normalization factors 1402 may becomputed to correct for irregularities in the look-up table response.These normalization factors 1402 may be applied to measured vectorhistograms. Ideally, all of the normalization factors would be equal to1.

FIG. 15 is a diagram 1500 depicting an angle calculation histogram table1502 for a plurality of regions 1504 of an image 1506 which depicts anangled one-dimensional barcode. As shown, each of the regions 1504produces a histogram. In this example, every histogram in the histogramtable 1502 is single mode with a centroid between 6 and 7. For example,the directional values of one of the regions is shown at arrow 1508 ashaving a centroid of 6.753, which corresponds to an angle calculation of152 degrees (i.e., 6.753 directional value multiplied by 22.5 degreesper directional value=152 degrees).

FIG. 16 is a flow diagram of a method 1600 of operation for an imageprocessor system to detect text, one-dimensional machine-readablesymbols and two dimensional machine-readable symbols in an image. Inoperation, the method 1600 may analyze the histograms discussed above.

At 1602, at least one processor may receive the 16×16 regions from thevector map, as discussed above. At 1604, the at least one processorfinds the two largest histogram values.

At 1606, the at least one processor determines whether either of the twolargest histogram values is equal to the directional value 8. As notedabove, the directional value of 8 is reserved for 3×3 pixelconfigurations which have multiple directions. If either of the twolargest histogram values is equal to 8 (i.e., 1606=“yes”), then at 1608the at least one processor determines whether a binarization score isbelow a two-dimensional binarization threshold (e.g., 80). If thebinarization score is below the two dimensional binarization threshold(i.e., 1608=“yes”), the at least one processor determines the regionincludes text at 1610. If the binarization score is not below thetwo-dimensional binarization threshold (i.e., 1608=“no”), the at leastone processor determines the region includes a two-dimensionalmachine-readable symbol at 1620.

If neither of the two largest histogram values are equal to 8 (i.e.,1606=“no”), then at 1612 the at least one processor determines if thereis only one direction present. If there is only one direction present,the at least one processor determines that a one-dimensionalmachine-readable symbol is present. At 1614, the at least one processormultiplies the directional value (i.e., 0 to 7) by 22.5 degrees toobtain the angle for the one-dimensional machine-readable symbol at1616.

If there is more than one direction present (i.e., 1612=“no”), the atleast one processor then determines whether the delta between theindices is 1 or 7 at 1618. If the delta between the indices is 1 or 7,then at 1608 the at least one processor determines whether abinarization score is below a two-dimensional binarization threshold(e.g., 80).

If the binarization score is below the two dimensional binarizationthreshold (i.e., 1608=“yes”), the at least one processor determines theregion includes text at 1610. If the binarization score is not below thetwo-dimensional binarization threshold (i.e., 1608=“no”), the at leastone processor determines the region includes a two-dimensionalmachine-readable symbol at 1620.

If the delta between the indices is not 1 or 7 (i.e., 1618=“no”), thenthe at least one processor determines whether the ratio of all 9directional values versus just the two largest directional values isgreater than a threshold (e.g., 0.2) at 1622. If so (i.e., 1622=“yes”),then control passes to decision act 1608.

If the ratio of all 9 directional values versus just the two largestdirectional values is not greater than a threshold (i.e., 1622=“no”),then the at least one processor computes the centroid of the two largesthistogram values at 1624. At 1614, the result is multiplied by 22.5degrees to obtain the one dimensional machine-readable symbol anglevalue at 1616.

As discussed above, the systems and methods of the present disclosureprovide an effective method for identifying valid machine-readablesymbol regions of interest in real time, thereby removing this burdenfrom a decode engine and greatly improving process times. This sameprocedure also provides a substantial benefit for image transmission,which can be a serious bottleneck for current machine-readable symbolreader designs. Further, the systems and methods disclosed herein may beparticularly well suited for implementation on a field programmable gatearray (FPGA).

The region of interest location methodology and compression methodologydiscussed herein may not necessarily be coupled together. For example,the region of interest location methodology may be used to improvedecoding and the unprocessed image data may be passed through to be usedfor other applications, such as video coding, for example.

The foregoing detailed description has set forth various implementationsof the devices and/or processes via the use of block diagrams,schematics, and examples. Insofar as such block diagrams, schematics,and examples contain one or more functions and/or operations, it will beunderstood by those skilled in the art that each function and/oroperation within such block diagrams, flowcharts, or examples can beimplemented, individually and/or collectively, by a wide range ofhardware, software, firmware, or virtually any combination thereof. Inone implementation, the present subject matter may be implemented viaApplication Specific Integrated Circuits (ASICs). However, those skilledin the art will recognize that the implementations disclosed herein, inwhole or in part, can be equivalently implemented in standard integratedcircuits, as one or more computer programs running on one or morecomputers (e.g., as one or more programs running on one or more computersystems), as one or more programs running on one or more controllers(e.g., microcontrollers) as one or more programs running on one or moreprocessors (e.g., microprocessors), as firmware, or as virtually anycombination thereof, and that designing the circuitry and/or writing thecode for the software and or firmware would be well within the skill ofone of ordinary skill in the art in light of this disclosure.

Those of skill in the art will recognize that many of the methods oralgorithms set out herein may employ additional acts, may omit someacts, and/or may execute acts in a different order than specified.

In addition, those skilled in the art will appreciate that themechanisms taught herein are capable of being distributed as a programproduct in a variety of forms, and that an illustrative implementationapplies equally regardless of the particular type of signal bearingmedia used to actually carry out the distribution. Examples of signalbearing media include, but are not limited to, the following: recordabletype media such as floppy disks, hard disk drives, CD ROMs, digitaltape, and computer memory.

The various implementations described above can be combined to providefurther implementations. To the extent that they are not inconsistentwith the specific teachings and definitions herein, all of the U.S.patents, U.S. patent application publications, U.S. patent applications,foreign patents, foreign patent applications and non-patent publicationsreferred to in this specification, including U.S. patent applicationtitled “SYSTEM AND METHOD FOR READING MACHINE READABLE CODES INTRANSPORTATION AND LOGISTIC APPLICATIONS,” filed on May 29, 2015(attorney docket number DLIA.004US), are incorporated herein byreference, in their entirety. Aspects of the implementations can bemodified, if necessary, to employ systems, circuits and concepts of thevarious patents, applications and publications to provide yet furtherimplementations.

These and other changes can be made to the implementations in light ofthe above-detailed description. In general, in the following claims, theterms used should not be construed to limit the claims to the specificimplementations disclosed in the specification and the claims, butshould be construed to include all possible implementations along withthe full scope of equivalents to which such claims are entitled.Accordingly, the claims are not limited by the disclosure.

1. A method of operation for an image processor system, the methodcomprising: receiving, by at least one processor, an original image filefrom at least one nontransitory processor-readable medium, the originalimage file comprising a two dimensional array of pixels, each of thepixels having an original pixel value; partitioning, by the at least oneprocessor, the original image file into a plurality of two dimensionalregions, each of the plurality of regions comprising a two dimensionalarray of the pixels; binarizing, by the at least one processor, theoriginal image file to generate a binarized image file comprising a twodimensional array of binarized pixels, each of the binarized pixelshaving a binarized pixel value; correlating, by the at least oneprocessor, each of the binarized pixels in the binarized image file by,for each binarized pixel, evaluating a binarization state of at leastone neighboring pixel; identifying, by the at least one processor, whichof the binarized pixels in the binarized image have a correlation valueabove a correlation threshold; generating, by the at least oneprocessor, a region correlation array comprising a plurality of regioncorrelation values, each region correlation value corresponding to oneof the plurality of regions and indicative of the number of binarizedpixels in the corresponding region identified as having a correlationvalue above the correlation threshold; for each region, comparing, bythe at least one processor, the region correlation value with a regionof interest threshold; and identifying, by the at least one processor,the region as a region of interest if the region correlation value isabove the region of interest threshold.
 2. The method of claim 1 whereinbinarizing the original image file comprises implementing an edgedetection algorithm to generate a binarized image file comprising a twodimensional array of binarized pixels, each of the binarized pixelshaving a binarized pixel value indicative of whether the binarized pixelhas been classified as an edge by the edge detection algorithm.
 3. Themethod of claim 1, further comprising: blurring, by the at least oneprocessor, the region correlation array using a convolution operation togenerate a blurred region correlation array comprising a plurality ofblurred region correlation values; wherein, for each region, comparingthe region correlation value with a region of interest thresholdcomprises comparing the blurred region correlation value with a regionof interest threshold; and identifying the region as a region ofinterest if the region correlation value is above the region of interestthreshold comprises identifying the region as a region of interest ifthe blurred region correlation value is above the region of interestthreshold.
 4. The method of claim 1, further comprising: responsive toidentifying the region as a region of interest, identifying, by the atleast one processor, each region adjacent the region as a region ofinterest.
 5. The method of claim 1, further comprising: computing, bythe at least one processor, a region average pixel value for each of aplurality of non-regions of interest, each non-region of interest beingone of the regions of the original image file not identified as a regionof interest; for each pixel in a non-region of interest, setting, by theat least one processor, the pixel value to the computed region averagepixel value for the region of which the pixel is a part to generate amodified image file; and storing, by the at least one processor, themodified image file in the at least one nontransitory processor-readablemedium.
 6. The method of claim 1, further comprising: sending, by the atleast one processor, the modified image file to a decoder for decodingof the modified image file.
 7. The method of claim 1, furthercomprising: decoding, by the at least one processor, the modified imagefile.
 8. The method of claim 1, further comprising: compressing, by theat least one processor, the modified image file to generate a compressedmodified image file.
 9. The method of claim 8, further comprising:sending, by the at least one processor, the compressed modified imagefile to a processor-based device over at least one data communicationchannel.
 10. The method of claim 8 wherein compressing the modifiedimage file comprises, for each non-region of interest of the originalimage file, storing, by the at least one processor, a single pixel valuefor all of the pixels in the non-region of interest.
 11. An imageprocessor system, comprising: at least one processor; at least onenontransitory processor-readable storage medium operatively coupled tothe at least one processor which stores an original image filecomprising a two dimensional array of pixels, each of the pixels havingan original pixel value, the at least one nontransitoryprocessor-readable medium further storing at least one of data orinstructions which, when executed by the at least one processor, causethe at least one processor to: partition the original image file into aplurality of two dimensional regions, each of the plurality of regionscomprising a two dimensional array of the pixels; identify one or moreregions of interest in the original image file, the one or more regionsof interest comprising one or more regions wherein data of interest arelikely to be found; compute a region average pixel value for each of aplurality of non-regions of interest, each non-region of interest beingone of the regions of the original image file not identified as a regionof interest; for each pixel in a non-region of interest, set the pixelvalue to the computed region average pixel value for the region of whichthe pixel is a part to generate a modified image file; and store themodified image file in the at least one nontransitory processor-readablemedium.
 12. The image processor system of claim 11 wherein the at leastone processor: binarizes the original image file to generate a binarizedimage file comprising a two dimensional array of binarized pixels, eachof the binarized pixels having a binarized pixel value; correlates eachof the binarized pixels; identifies which of the binarized pixels have acorrelation value above a correlation threshold; and generates a regioncorrelation array comprising a plurality of region correlation values,each region correlation value corresponding to one of the plurality ofregions and indicative of the number of binarized pixels in thecorresponding region identified as having a correlation value above thecorrelation threshold.
 13. The image processor system of claim 12wherein the at least one processor: for each of the binarized pixels,evaluates a binarization state of at least one neighboring pixel tocorrelate the binarized pixel.
 14. The image processor system of claim12 wherein the at least one processor: blurs the region correlationarray using a convolution operation to generate a blurred regioncorrelation array of blurred region correlation values.
 15. The imageprocessor system of claim 12 wherein the at least one processor: foreach region: compares the region correlation value with a region ofinterest threshold; and identifies the region as a region of interest ifthe region correlation value is above the region of interest threshold.16. The image processor system of claim 15 wherein the at least oneprocessor: for each region in which the region correlation value isabove the region of interest threshold, identifies each region adjacentthe region as a region of interest.
 17. The image processor system ofclaim 11 wherein the data of interest comprises at least one of text ora machine-readable symbol.
 18. The image processor system of claim 11wherein the at least one processor: sends the modified image file to adecoder for decoding of the data of interest present in the modifiedimage file.
 19. The image processor system of claim 11 wherein the atleast one processor: decodes the data of interest present in themodified image file.
 20. The image processor system of claim 11 whereinthe at least one processor: compresses the modified image file togenerate a compressed modified image file.
 21. The image processorsystem of claim 20 wherein the at least one processor: sends thecompressed modified image file to a processor-based device over at leastone data communication channel.
 22. The image processor system of claim20 wherein the at least one processor: for each non-region of interestof the original image file, stores a single pixel value for all of thepixels in the non-region of interest.
 23. The image processor system ofclaim 11 wherein the at least one processor: sends the modified imagefile to a processor-based device via at least one data communicationchannel.
 24. A method of operation for an image processor system, themethod comprising: receiving, by at least one processor, an originalimage file from at least one nontransitory processor-readable medium,the original image file comprising a two dimensional array of pixels,each of the pixels having an original pixel value; partitioning, by theat least one processor, the original image file into a plurality of twodimensional regions, each of the plurality of regions comprising a twodimensional array of the pixels; identifying, by the at least oneprocessor, one or more regions of interest in the original image file,the one or more regions of interest comprising one or more regionswherein data of interest are likely to be found; computing, by the atleast one processor, a region average pixel value for each of aplurality of non-regions of interest, each non-region of interest beingone of the regions of the original image file not identified as a regionof interest; for each pixel in a non-region of interest, setting, by theat least one processor, the pixel value to the computed region averagepixel value for the region of which the pixel is a part to generate amodified image file; and storing, by the at least one processor, themodified image file in the at least one nontransitory processor-readablemedium.
 25. The method of claim 24 wherein identifying one or moreregions of interest in the original image file comprises: binarizing, bythe at least one processor, the original image file to generate abinarized image file comprising a two dimensional array of binarizedpixels, each of the binarized pixels having a binarized pixel value;correlating, by the at least one processor, each of the binarizedpixels; identifying, by the at least one processor, which of thebinarized pixels have a correlation value above a correlation threshold;and generating, by the at least one processor, a region correlationarray comprising a plurality of region correlation values, each regioncorrelation value corresponding to one of the plurality of regions andindicative of the number of binarized pixels in the corresponding regionidentified as having a correlation value above the correlationthreshold.
 26. The method of claim 25 wherein correlating each of thebinarized pixels comprises, for each of the binarized pixels,evaluating, by the at least one processor, a binarization state of atleast one neighboring pixel.
 27. The method of claim 25 whereinidentifying one or more regions of interest in the original image filecomprises blurring, by the at least one processor, the regioncorrelation array using a convolution operation to generate a blurredregion correlation array of blurred region correlation values.
 28. Themethod of claim 25 wherein identifying one or more regions of interestin the original image file comprises, for each region: comparing, by theat least one processor, the region correlation value with a region ofinterest threshold; and identifying, by the at least one processor, theregion as a region of interest if the region correlation value is abovethe region of interest threshold.
 29. The method of claim 28 whereinidentifying one or more regions of interest in the original image filecomprises, for each region in which the region correlation value isabove the region of interest threshold, identifying, by the at least oneprocessor, each region adjacent the region as a region of interest. 30.The method of claim 24 wherein identifying one or more regions ofinterest in the original image file comprises identifying, by the atleast one processor, one or more regions of interest comprising one ormore regions wherein data of interest are likely to be found, and thedata of interest comprises at least one of text or a machine-readablesymbol.
 31. The method of claim 24, further comprising: sending, by theat least one processor, the modified image file to a decoder fordecoding of the data of interest present in the modified image file. 32.The method of claim 24, further comprising: decoding, by the at leastone processor, the data of interest present in the modified image file.33. The method of claim 24, further comprising: compressing, by the atleast one processor, the modified image file to generate a compressedmodified image file.
 34. The method of claim 33, further comprising:sending, by the at least one processor, the compressed modified imagefile to a processor-based device over at least one data communicationchannel.
 35. The method of claim 33 wherein compressing the modifiedimage file comprises, for each non-region of interest of the originalimage file, storing, by the at least one processor, a single pixel valuefor all of the pixels in the non-region of interest.
 36. The method ofclaim 24, further comprising: sending, by the at least one processor,the modified image file to a processor-based device via at least onedata communication channel.